10.5281/zenodo.6250609
https://zenodo.org/records/6250609
oai:zenodo.org:6250609
Molloy, Laura
Laura
Molloy
0000-0002-5214-4466
CODATA
Gregory, Arofan
Arofan
Gregory
0000-0003-1067-7659
DDI / CODATA
Kulla, Kaia
Kaia
Kulla
Statistics Estonia
Danciu, Alina
Alina
Danciu
0000-0002-5126-0078
Sciences Po
Data Quality: Thinking about Quality and DDI Metadata
Zenodo
2021
research data
data quality
metadata
CODATA
DDI
2021-11-18
eng
Presentation
https://codata.org/initiatives/data-skills/ddi-training-webinars/
10.5281/zenodo.6250608
https://zenodo.org/communities/codata
https://zenodo.org/communities/dcc-rdm-training-materials
https://zenodo.org/communities/ddi-train
1
Creative Commons Attribution 4.0 International
Data quality is a topic which is often discussed and attracts a lot of interest, but the way in which it is approached varies widely. This, our fourth webinar of the 2021 series took place on 18 November 2021 and examined different approaches in the use of metadata for describing data quality from the perspective of data producers in official statistics and in the scientific and research domains, and how DDI fits into this picture.
Some data providers will attempt to assert their data quality as a function of certification for the services they provide, and the richness of their metadata. In the official statistics world, additional descriptive metadata is provided according to agreed quality frameworks. DDI supports both approaches, and the details of each are explored in this webinar.
Presenters were:
Arofan Gregory, Chair, CDI Working Group, DDI Alliance / CODATA
Kaia Kulla, Statistics Estonia
The session was introduced by Laura Molloy, CODATA; and the question and answer session was moderated by Alina Danciu, Sciences Po.
Data quality is a topic which is often discussed and attracts a lot of interest, but the way in which it is approached varies widely. This, our fourth webinar of the 2021 series took place on 18 November and examined different approaches in the use of metadata for describing data quality from the perspective of data producers in official statistics and in the scientific and research domains, and how DDI fits into this picture.
Some data providers will attempt to assert their data quality as a function of certification for the services they provide, and the richness of their metadata. In the official statistics world, additional descriptive metadata is provided according to agreed quality frameworks. DDI supports both approaches, and the details of each are explored in this webinar.
Presenters were:
Arofan Gregory, Chair, CDI Working Group, DDI Alliance / CODATA
Kaia Kulla, Statistics Estonia
The session was introduced by Laura Molloy, CODATA; and the question and answer session was moderated by Alina Danciu, Sciences Po.
Data Privacy Statement for participants.
About DDI
DDI Alliance: https://ddialliance.org/
Current products: https://ddialliance.org/products/overview-of-current-products
About DDI-CDI
Introduction: https://ddi-alliance.atlassian.net/wiki/download/attachments/860815393/Part_1_DDI-CDI_Intro_PR_1.pdf
Public review page: https://ddi-alliance.atlassian.net/wiki/x/IQBPMw
Complete download package: https://ddi-alliance.bitbucket.io/DDI-CDI/DDI-CDI_Public_Review_1.zip
Announcement at DDI Alliance website: https://ddialliance.org/announcement/public-review-ddi-cross-domain-integration-ddi-cdi